Double muon and double electron samples are used for the main analysis, single muon samples are used for the efficiency correction estimation steps. Other samples are used for the backgrounds estimation purposes.

Relevant software: CMSSW_5_3_3_patch2

Use latest global tags for MC data for a given release as documented here

Note: that for proper compilation slc5 machine is necessary, and the code might not compile out of the box on slc6 or later CMSSW release versions (it would need to be ported first).

To simply perform a local test of the ntuple-maker run:

cmsRun ntuple_cfg.py

to produce the ntuples over full dataset use CRAB:

crab -create -submit -cfg crab.cfg
crab -get all -c <crab_0_datetime>

Notes on the ntuples used

The ntuples of different TTree format are used in the analysis. First, the Purdue flat format: it contains branches of fundamental data types like int, bool and float. It stored the branches used for the dimuon analysis only, and is optimized for storage - it is possible to fit in the scratch space as the total size of the ntuples is within 2TB. The signal and background MC samples are located at: /mnt/hadoop/store/user/asvyatko/DYstudy/dataAnalysis13/rootfiles/, and the data ntuples are located at: /mnt/hadoop/store/group/ewk/DY2013/:

2) In /mnt/hadoop/store/user/asvyatko/DYstudy/dataAnalysis13/rootfiles/

drwxrwxr-x 83 cms1005 root 4096 Nov 1 2013 DYM1000_PUOct

drwxrwxr-x 398 cms1005 root 4096 Nov 7 2013 DYM1020_PUOct_FULL

drwxrwxr-x 83 cms1005 root 4096 Nov 1 2013 DYM1500_PUOct

drwxrwxr-x 83 cms1005 root 4096 Nov 1 2013 DYM2000_PUOct

drwxrwxr-x 83 cms1005 root 4096 Nov 1 2013 DYM200_PUOct

drwxrwxr-x 428 cms1005 root 4096 May 20 2014 DYM20_MadGraph_Light

drwxrwxr-x 1001 cms1005 root 4096 Nov 1 2013 DYM20_PUOct_Stefano

drwxrwxr-x 83 cms1005 root 4096 Nov 1 2013 DYM400_PUOct

drwxrwxr-x 83 cms1005 root 4096 Nov 1 2013 DYM500_PUOct

drwxrwxr-x 83 cms1005 root 4096 Nov 1 2013 DYM700_PUOct

drwxrwxr-x 83 cms1005 root 4096 Nov 1 2013 DYM800_PUOct

drwxrwxr-x 103 cms1005 root 4096 May 14 2014 DYtautau1020_Light

drwxrwxr-x 463 cms1005 root 4096 May 17 2014 DYtautau20_Light

drwxrwxr-x 420 cms1005 root 4096 May 14 2014 WJets_Light

drwxrwxr-x 203 cms1005 root 4096 May 7 2014 WWJetsTo2L2Nu_Light

drwxrwxr-x 42 cms1005 root 4096 May 10 2014 WZJets3LNu_Light

drwxrwxr-x 62 cms1005 root 4096 May 9 2014 WZJetsTo2L2Q_Light

drwxrwxr-x 22 cms1005 root 4096 May 7 2014 ZZJetsTo2L2Nu_Light

drwxrwxr-x 39 cms1005 root 4096 May 7 2014 ZZJetsTo2L2Q_Light

drwxrwxr-x 324 cms1005 root 4096 May 9 2014 ZZJetsTo4L_Light

drwxrwxr-x 86 cms1005 root 4096 May 9 2014 tW_Light

drwxrwxr-x 83 cms1005 root 4096 May 8 2014 tbarW_Light

drwxrwxr-x 204 cms1005 root 4096 May 6 2014 tt1000_Light

drwxrwxr-x 211 cms1005 root 4096 May 7 2014 tt700_Light

drwxrwxr-x 203 cms1005 root 4096 May 8 2014 ttjets_v1_Light

drwxrwxr-x 256 cms1005 root 4096 May 7 2014 ttjets_v2_Light

Second, the Purdue objectified format which contains both electron and muon branches. The tree contains 101 branches, and the average compression factor of the tree is 3.50.It is not as efficient in the analysis and requires relatively more time to process as must be stored on the fuse mounted partition. It's size is about 5TB. It has 165 branches in the tree, and the average tree compression factor is 35.69. Ntuples of this type are located at: /mnt/hadoop/store/group/ewk/DY2013/

Finally the MIT ntuple format which contains electron, photon and e-mu branches stored as TClonesArrays of objects, it's size is very small (about 1TB), and it is stored here: /mnt/hadoop/store/user/asvyatko/DYstudy/dataAnalysis13/MITrootfiles. It contains over 311 branches in the tree, and the average tree compression factor is 2.14.

To get started with FEWZ follow this nice tutorial dedicated to exactly this topic: https://twiki.cern.ch/twiki/bin/viewauth/CMS/FEWZHowToUse_QuickTutorial. It goes in depth on the setting up the environment, preparing configuration files etc. After the FEWZ cross sections are prepared, there is few other things one needs to do with it. In depth understanding of this tutorial is a prerequisite to work with FEWZ.

First, parse the raw output files to the maps, which can be used in the further analysis. The files have name of that form: 'NNLO.NNLO*dat'. The output FEWZ files which serve as an input for all the macros below can be found at

Once the ntuples are ready, one can proceed to the actual physics analysis. The first step of the analysis is the event selection. Currently, we use the so-called cut-based approach to discriminate between signal and background. For more on event selection read chapter 5 in the analysis note CMS-AN-13-420. Before starting to run a macro, set up the working area. Find all the necessary scripts in:

cd $DYWorkDir/test/ControlPlots

The code for event selection consists of 3 main files (and a few auxiliary). First of all the TSelector class which is customized for event selection used in a given analysis, necessary weights (pileup, FEWZ and momentum scale correction) are applied in the macro. The Monte-Carlo weights are also hardcoded inside the macro for each MC sample used. Next, is the wrapper ROOT macro which calls the TSelector to run on a given dataset. This wrapper is shown below, and explained step-by-step:

//macro takes 3 arguments, which are passed from the python script. These are: the histogram name (invariant mass, or for instance rapidity), ntuple weight or/custom (this option is deprecated - we always use custom weight), and the type of momentum scale correction (also deprecated - the correction does not depend on the run range in 8 TeV analysis)
void analyseYield(const char* WHICHHIST, const char* NTUPLEWEIGHT, const char* MOMCORRTYPE) {
// Depending on the directory with data, the protocol used to access data will be different: "file" or "xrootd" are the most commonly used.
TString protocol = "file://";
//TString protocol = "root://xrootd.rcac.purdue.edu/";
//Pointer to the location of the data used. Can be on /mnt/hadoop or on the scratch
TString dirname = "/mnt/hadoop/store/group/ewk/DY2013/";
// Next, the TFileCollection is created. This section is specific for each dataset: data or MC, so we prepare this wrapper macro for each sample
TFileCollection* c1 = new TFileCollection("data","data");
//Splitting criteria by runs/eras is happening here switch to RunAB, RunC, RunD. This is handy for studies of run dependencies
if (MOMCORRTYPE == "RunAB") c1->Add(protocol+dirname+"Data_RunAJan2013_Oct"+"/*.root");
if (MOMCORRTYPE == "RunAB") c1->Add(protocol+dirname+"Data_RunBJan2013_Oct_p1"+"/*.root");
if (MOMCORRTYPE == "RunAB") c1->Add(protocol+dirname+"Data_RunBJan2013_Oct_p2"+"/*.root");
if (MOMCORRTYPE == "RunC1") c1->Add(protocol+dirname+"Data_RunCJan2013_Oct_p1"+"/*.root");
if (MOMCORRTYPE == "RunC2") c1->Add(protocol+dirname+"Data_RunCJan2013_Oct_p2"+"/*.root");
if (MOMCORRTYPE == "RunD1") c1->Add(protocol+dirname+"Data_RunDJan2013_Oct_p1"+"/*.root");
if (MOMCORRTYPE == "RunD2") c1->Add(protocol+dirname+"Data_RunDJan2013_Oct_p2"+"/*.root");
//Set the location of ProofLite Sandbox. It is more convenient to use the custom path rather than $HOME/.proof
gEnv->SetValue("ProofLite.Sandbox", "<path to your working dir>/test/ControlPlots/proofbox/");
//splitting criteria: how many worker nodes to use for the run: using more than 10-15 nodes usually will cause instability and lead to a crash subsequently
TProof* p = TProof::Open("workers=20");
p->RegisterDataSet("DATA", c1,"OV");
p->ShowDataSets();
//Deprecated - just leave as is, always
TObjString* useNtupleWeightFlag = new TObjString(NTUPLEWEIGHT);
p->AddInput(new TNamed("useNtupleWeightFlag",NTUPLEWEIGHT));
//The histogram should always be "invm" - it will give both 1D and 2D histograms. But if one needs to study N-1 selection, then the string should be the name of the cut to exclude
TObjString* histogramThis = new TObjString(WHICHHIST);
p->AddInput(new TNamed("histogramThis",WHICHHIST));
//This is now useless, but for later studies it might become useful again, if there is a run dependency for the momentum scale correction
TObjString* momCorrType = new TObjString(MOMCORRTYPE);
p->AddInput(new TNamed("momCorrType",MOMCORRTYPE));
gROOT->Time();
p->SetParameter("PROOF_LookupOpt", "all");
//This invokes the TSelector: "recoTree/DiMuonTree" is the name of the ROOT tree inside the file, "EventSelector_CP.C" is the name os the TSelector
p->Process("DATA#/recoTree/DiMuonTree","EventSelector_CP.C+");
}

There is one extra level here - the python script. It calls the above ROOT wrapper macro and typically looks like this:

#!/usr/bin/env python
from subprocess import Popen
#This normally is just "imvm", but for N-1 control plots like 18-25 in the AN-13-420 one needs to set to a custom cut name, for instance: 'relPFisoNoEGamma','chi2dof','trackerHits','pixelHits','CosAngle','muonHits','nMatches','dxyBS','relPFisoNoEGamma','vtxTrkProb','trigMatches','pT','eta']
histos = ['invm']
#normally one needs to run over all of them. Splitting to a set of runs is useful because loading very large number of files into one session can cause instability
eras = ['RunAB','RunC1','RunC2','RunD1','RunD2']
#Simply invoke ROOT wrapper macro using Popen
for run in eras:
for hist in histos:
Popen('root -b -l -q \'analyseYield.C(\"'+hist+'\",\"False\",\"'+run+'\")\'',shell=True).wait()

Once this is understood, one can run the macro. To produce plots like 35-37 use the analyse.py macro, which calls the wrapper for TSelector for the DY analysis (as described above):

mkdir runfolder
python analyseYield_mc.py
python analyseYield_data.py

Important information about the reweightings. Pileup reweighing is accessed from the ntuple, directly from the branch on a per event basis. The FEWZ weights are extracted from theoretical calculation, and are provided as arrays inside the efficiencyWeightToBin2012.C file located in the same directory (or any other directory, as long as there is an appropriate include in the header of the TSelector). The FEWZ weights are looked up based on the GEN mass as follows inside the code, only for signal MC:

Few words about the normalization. The data events are not renormalized. The MC weights are weighted according to the probability of each event to be observed in a real collision event and according to the number of events generated in the sample. Therefore

For better accuracy we use the number of events actually ran on, rather than the number generated. We calculate it in the event loop, and apply it in the EventSelector::Terminate() method. In both the 7 and 8 TeV analysis, we normalized the MC tack (signal and backgrounds) to the number of events in data in the Z peak region (before the efficiency corrections). A special post-processing macro takes care of this:

python postprocessor.py
cp runfolder/stack* ../Inputs/rawYield

This python script adds up individual ROOT files with hadd and invokes ROOT macros parser.C and parser_2D.C which has a method for normalization of MC stack to data in the Z peak region.

After that, switch to the Dielectron working directory and produce necessary yield histograms before continuing with the style plotting. First, you will have to get a custom rootlogon file. Note: some of the libraries loaded in this rootlogon might interfere with the Proof environment in your machine.

cd ../FullChain
cp ../rootlogon_MIT.C ~/.rootlogon.C

Inspect the wrapper_EE.sh file inside and set the do_selection flag to 1 (true), and check the input files to run on are properly specified in the conf_file

//top of the file
filename_data="../config_files/test.conf"
//scroll down a little
do_selection=1
do_prepareYields=1

Then run in two steps: (1) produce reduced ntuples, (2) prepare binned yields for analysis

./wrapper_EE.sh

To switch between 1D and 2D cases open the ../Include/DYTools.hh file and change the flag to constint study2D=1;.

After that, the style macro is used to plot the publication quality plots.

cd ../style/DY
root -l plot.C

the style macro is used This would plot the 1D yields distribution (the switch between the electrons and muons is done manually inside the macro by adjusting the paths).

To plot the 2D distributions do:

root -l ControlPlots_2D.C

Step 4: Acceptance and Efficiency estimation

Another constituent of the cross-section measurement is the acceptance-efficiency.

Acceptance is determined using GEN level information

To be able to produce the acceptance and efficiency one needs to change to a different folder, and run a different TSelector. But the general flow TSelector->ROOT wrapper->python wrapper is almost the same:

cd $DYWorkDir/AccEffMCtruth
python analyseMCtruth.py

The script will produce the root file with histograms corresponding to the mass and rapidity spectra after the acceptance cuts, selection cuts or both which are then used to calculate the acceptances, efficiencies and acceptance-efficiency products with and without pileup and FEWZ reweighing by executing:

root -l plotMCtruth.C
root -l plotMCtruth_2D.C

To get the corresponding distributions in the electron channel change to FullChain folder:

cd ../FullChain
//doAcceptance = 1
//doEfficiency = 1
./wrapper_EE.sh

The macro output a root file starting with out1* or out2* containing the histograms corresponding to the acceptance, efficiency and their product. To produce the publication level plots, the style macro described in the previous section needs to be used again

cd ../style/DY
root -l plot.C

Note, that whenever you use the style/plot.C macro, you have to configure the mode inside. This looks something like this:

And you mark the histogram you would like to plot with the true. To get the 2D plots do:

root -l plot_acc_2D.C

Step 5: Data-driven efficiency correction

Only in the muon channel, the electron efficiency scale factors are obtained from the EGamma group, and not re-measured independently.

Next, the data-driven efficiency corrections are applied. This is done using the standard CMSSW recipe, so a lot of additional packages needs to be checked out. Follow this twiki: https://twiki.cern.ch/twiki/bin/viewauth/CMS/MuonTagAndProbe to set up your working area for the ntuple production (alternatively, one can use the trees already produced!)

The procedure goes in two steps: T&P tree production -> rerun seldom (ideally once), it depends only on the definitions of the tag and probe

After familiarizing yourself with the TagAndProbe package, you need to produce the muon efficiencies as a function of pT and eta. You can use the wrapper.py script specifying which variables to bin the efficiency in and what runs/MC samples to process.

Finally, produce the plots with

python auxPlotProducer.py

Step 5: Background estimation

QCD data driven background estimation

In 8 TeV analysis, the main method to estimate the QCD background in the dimuon channel is the ABCD method (the fake-rate method is used in the electron channel). Before starting, let me summarize the ABCD method in a nutshell:

ABCD method

1) choose 2 variables: assume two variables are independent

2) assume the fraction should be same if there is no correlation: N_A / N_B = N_C / N_D

3) In our study, use two variables: sign of muon pair, muon isolation

4) QCD fraction in each region has a dependence. We produce the correction factor for each region: B, C, D

5) Produce N_B, N_C, N_D from data sample, and estimate N_A from them at the end (applying the correction factors)

Now, let's go step by step.

First, change to the ABCD folder:

cd $DYWorkDir/ABCDmethod

The procedure consists of few steps and is guided by the wrapper.py script located inside the folder:

Thus, for each of the MC samples and for the real data a set of sequences is ran. First the QCDFrac_*.py, which invoke the EventSelector_Bkg.C TSelector class for various values of charge and isolation (the variables defining the signal and background regions), based on the histograms filled, the coefficients are calculated. Second, the qcdFracHadder.py scripts is ran on the on the output of the first step. It is a utility script which repacks the histograms in an appropriate format. Third, the ABCD2vari_init.py script which actually performs the etiolation of ABCD coefficients in each region. Finally, the ABCD2vari_*.py scripts invoke the EventSelector_Bkg2.C TSelector class, passing the ABCD coefficients as TObjString objects inside the macro.

The post-processing and the output harvesting step is performed by the following python script:

python abcdPostprocessor.py

It uses the output of the second TSelector as an inout, hadds it and produces a root file with th histogram which is then used in the analysis.

E-mu data-driven background estimation method

To estimate all the non-QCD backgrounds we employ the so-called e-mu data driven background estimation method. The same method is applied in the muon and electron channels. The code used for that purpose was originally adapted from Manny and it uses the so-called Bambu workflow. First, let's change into the e-mu working directory:

The above script needs to be ran twice in 2 modes: SS (same-sign pairs) and OS (opposite-sign pairs). The switch is don win the selectEmuEvents.C script by switching:

if (!isOppositeSign) continue; //if (isOppositeSign) continue;

And also changing the ntupDir name. One will have to edit the data_emu.conf to point to the local ntuples before running.

After running this step, the reduced ntuples should be output to a directory (../root_files/selected_events/DY/ntuples/EMU/). One would also need to run selectEvents.C to generate reduced electron ntuples.These ntuples must contain two branches, mass (dilepton invariant mass) and weight. After this is done, the e-mu macro can be ran:

#compile code
> gmake eMuBkgExe
#This should produce the binary eMuBkgExe. There are many options to run it. See the the possible options below
./eMuBkgExe #run emu method for 1D analysis and produce plots
./eMuBkgExe --doDMDY #run 2D analysis and produce plots
./eMuBkgExe --doDMDY --saveRootFile #same as above but output ROOT file with yield, statistical and systematic info as true2eBkgDataPoints.root

This macro is also ran in two regimes: using SS and OS ntuples as an input, and the proper true2eBackground file are produced and saved. The reason why we need to rerun SS and OS cases is because we rely on this for estimation of missing QCD contribution in the e-mu spectrum. These true2eBackground files and the dilepton yields serve as an input to the final step of e-mu background estimation, the production of a final root file with histograms:

As you can see, 4 different macros are re-ran for electrons, muons, 1D and 2D.

One other source of background considered in this analysis is the photon induced background. This background is irreducible and is not estimated based on MC. The bulk of the calculations of this background is done in FEWZ3, by switching the photon induced components on and off. Once the output files are ready, one can simply parse them, get the bin-by-bin correction:

python dimitriExtracter.py
python dimitriParser.py

Following scripts can be used to visualize and compare the PI background yields:

root -l PIvalidation.C

Once the correction is prepared in a root file, it is simply loaded in the shapeR plotting macro as discussed in the sections below.

Step 6: Unfolding

Unfolding is applied to correct for migration of entries between bins caused by mass resolution effects (FSR correction is taken into account as a separate step, although it also uses the unfolding technique). In 8 TeV analysis, we use the iterative Bayesian unfolding technique. Provides a common interface between channels for symmetry and ease in combination and systematic studies. Both the iterative Bayesian and matrix inversion technique (used in 7 TeV) are implemented and described below.

To do any unfolding with MC, this requires 3 things:

Producing the response matrix

Making the histogram of measured events

Making the true histogram & closure test

First, change to the unfolding working directory (common for electron and muons).

cd ../Unfold

The main steps for unfolding procedure go as follows:

1. Produce the response matrix.

2. To produce the unfolded yield

3. Visualize the yields and ratios of yields

The first step is rather time consuming, and is done by:

python ResMatrix.py

which takes care of the response matrix production for both the 1D and 2D cases. Inputs for these macros are ntuples, and the output is the ROOT file with the true and measured MC yields as well as the response matrix. To visualize the resulting response matrices do:

root -l Matrix.C

The above matrix access the ROOT file which we produced on the previous step and simply does drawing. There is a switch inside this macro allows to change 1D and 2D plots, written with a comment inline.

Once this is done, one can continue to apply the unfolding technique. Open the unfold.C file and familiarize yourself with various flags (pre-processor pragmas to be more precise), namely:

The input to this file is the true, measured MC yields and the response matrix as well as the observed, reconstructed signal and background MC yields. A possibility to read the data-driven backgrounds is also implemented and controlled by the DATA_DRIVEN_BKG flag.

Above is the default setting, as we use iterative Bayesian method as default for 8 TeV in both channels. First run in the closure test mode by setting the run to 'POWHEG' inside the wrapper unfold.py, then switch to '' flag which will do the actually unfolding – we do not make any distinction between the run ranges in 8 TeV as the scale corrections are run independent

python unfold.py

It outputs 1 ROOT file with the yields before and after unfolding.

Repeat all for 2D:

python unfold_2D.py

Similarly to 1D, it calls the unfold_2D.C macro which takes the true, measured MC yields and the response matrix as well as the observed, reconstructed signal and background MC yields as inputs. A possibility to read the data-driven backgrounds is also implemented and controlled by the DATA_DRIVEN_BKG flag. It outputs 1 ROOT file with the yields before and after unfolding.

Then to repeat for electrons change to the FullChain folder and set the appropriate flags for runnings the response matrix production step

cd ../FullChain
vim wrapper_EE.sh
do_unfolding=1

That summarizes the unfolding step, and the output of this step will be used on the following analysis steps.

Step 7: FSR correction

The effect of FSR is manifested by photon emission off the final state lepton. It leads to a change of the dimuon invariant mass and as a result a dilepton has invariant mass distinct from the propagator (or Z/gamma*) mass.

For our analysis we estimate the effect of FSR and the corresponding correction by estimating the unfolding correction in invariant mass and rapidity bins. This is done by applying an exact same unfolding procedure as for the mass resolution effects described above. A minor difference is that we also apply bin-by-bin corrections for event classes that do not enter the response matrix.

Change to the FSRunfold directory

cd ../FSRunfold

And run the similar steps as above:

python FSRResMatrix.py

This script will give you the response matrix in 1D and 2D and also additional bin-by-bin corrections for events not entering the response matrix. In addition, there is an option to run fully bin-by-bin as a cross check. If you inspect the contents of this python script, you will be able too understand what actually is done. After the jobs are complete, you need to merge the individual root files using hadd, and then run the fracEff.C script to extract the additional corrections:

root -l fracEff.C

Similarly to the detector resolution unfolding step, you can inspect the response matrix:

root -l Matrix.C

To get these all quantities in the electron channel, similarly to the detector resolution unfolding case you just need to:

Step 8: Cross section calculation

Once all the constituents of the cross section are in place, one can continue with the cross section calculation results. First, the results are calculated in each individual channel and then they are combined using the BLUE method (as described in the Step 10 section). To calculate the 1D cross section in muon channel change to:

cd ../ShapeR
python shapeDY.py

this will produce an output root file in the ../Outputs directory. All the necessary input files are expected to be available in the ../Inputs directory. To get the 2D cross section change to

cd ../shapeR2D
python shapeR2D.py

The output file is also going to be created in the ../Outputs directory. To get the electron cross section do as usually:

cd ../FullChain
//set do_crossSectionFsr=1
./wrapper_EE.sh

One would have to rerun this step twice switching the flag between 1D and 2D.

This produces the necessary root files with the histograms of the cross section and uncertainties

Step 9: Covariance matrix calculation

The covariance matrix gives the uncertainties of the measurements together with the correlations between the analysis bins and different systematic sources. There are several distinctive steps in the covariance analysis: (1) calculation of the unfolding covariance matrix, (2) calculation of the efficiency covariance matrix and (3) the calculation of the FSR covariance matrix. The code necessary for this calculations is available in:

cd ../Covariance

Note, that this approach is working well only for the muon channel, for electron channel the result is produced by Andrius and electron group using a different technique.

The inputs necessary for the covariance matrix calculation derived on the previous steps are (1) efficiency correlation matrix produced with EffCorrAndSyst code package, (2) mass resolution unfolding response matrix, (3) FSR response matrix, (4) systematic uncertainty tables in ASCII format, (5) optionally the theory ROOT files are used if one is looking for chi2 tests. Change to the covariance directory:

cd ../Covariance

The open the ROOT session and locad the covariance matrix macro:

root -l
.L covariantMatrix.C

The consecutively call the "write" functions in the same ROOT session:

Here, the writeEffCorrToRootFile(case) stores efficiency covariance in covMatrices_case_store.root, the

writeShapeToRootFile(case) stores data in shape_r_case_store.root, the

writeSystToRootFile(case) stores data in shape_rSyst_case_store.root and

writeTheoryToRootFile(case) stores predictions in shape_r_Theory_case_store.root (the latter is needed for chi2 tests only).

The above files are used by estimateCovMatrix(case) which does the actual covariance matrix estimation:

estimateCovMatrix()

which produces covariance_finalResults_case.root. As specified in the text one can pass the argument to the function (1D, 2D, normalized or unnormalized), but I prefer to change the defaults and run with nor argument - this is either to keep track of your steps. The arguments (with default values) are "2D"/"1D", "mu"/"el","inAcc"/"fullAcc", "preFSR"/"postFSR". Only relevant combinations are covered ("2D" with "el" will crash).

After covariance matrix is produced, one can visualize it with:

root -l quickPlotter.C
root -l drawNicePlots_DYStoyan.C

The final step of the covariance matrix analysis is production of the ASCII files for HEPDATA or for BLUE studies.

python wrapper.py
python combine.py

Step 10: Electron-muon combination with the BLUE method

Having the root files for individual cross section measurements i the dielectron and dimuon channels, we need to combine them for a higher precision. The combination is performed with the BLUE method, which takes 2 vectors of measured values of the cross section and the covariance matrices.

First of all, switch the rootlogon for this task:

cp ../../rootlogon_defaultgluon.C ~/.rootlogon.C

Next, we need to make sure that the inputs are in the form the BLUE macro expects it (i.e. ASCII, not root):

cd ../BLUE
python bluePrinter.py
python bluePrinter_2D.py

This macro takes a ROOT file as an input and outputs the ASCII file as an output. The input ROOT files are produced using macros in the ShapeR and shapeR2D folders and supposed to be in the ../Outputs directory.

We can use the txt2Plot.py macro to validate the txt input by visualizing it.

After we have the inputs in proper format, we just need to run the resultCombiner.C macro. To pass all the inputs properly (which should be in the current folder), we specify them in the wrapper.py script and run it as

python wrapper.py
python wrapper_2D.py

The output will be the ASCII format again, but we normally need it in root. So we have to run another converter file after we finished:

python outToPeople.py
python outToPeople_2D.py

After that, we have the root file with the cross section histogram of the same format as we have for the individual cross sections, and we can visualize it (produce a plot for the publication) on the same step as we did for other cross sections in the previous section

Once the cross actions have been obtained, the double ratios – ratios of the normalized differential and double-differential cross sections – can be calculated. Most of the macros for the double ratio calculation is located in the ../ShapeR folder, so first change to that folder:

cd ../ShapeR

Given the input ROOT files with the cross sections at 8 TeV, and the old archived ROOT files with the 7 TeV results, the only thing we need to produce in addition is the uncertainties properly combined. This is done by the following few macros (note that the 1D uncertainties file is automatically produced by the ShapeR/shapeDY.py macro, so need to worry about it):

python uncertEE.py
python uncert_2D.py
python uncertEE_2D.py

The electron uncertainties are produced from the input files provided by the electron group. They are combined with th acceptance and modeling uncertainties provided by us.

Once the uncertainties are prepared, the double ratios can be produced. The macro does what the name intuitively suggests, it calculates the ratio of the cross sections per mass/rapidity bin from the 8 and 7 TeV normalized cross sections, and assigns proper uncertainty which it takes from the output of the previous step.

The final results are the absolute cross sections in bins of mass and rapidity in dielectron, dimuon channels and combination. As well as the double ratios. To plot the 1D differential cross sections do:

cd ../ShapeR

root -l plot_Comb.C

root -l plot_EE.C

root -l plot_MuMu.C

To plot the 1D double ratios (switch between the lepton channels is inside):

This requires running FEWZ to get the cross sections as a function of PT. Once the cross section maps are produced it can be visualized. The binning needs to be adjusted between plots 6-9 inside the macro.

Proper histogram needs to be checked in the list called histos. In addition, the root files produced by default are not going to contain proper N-1 filled histograms for the variables used for the selection. So one needs to perform a separate rerun for each variable (suggested to leave this plot for the end).

The procedure to prepare the scale factors is rather lengthy, but once the FEWZ cross section are available, one can parse them into root file and plot with the .C macros available in the folder (see above tutorial for more details)